Cascading architecture has been widely adopted in large-scale advertising systems to balance efficiency and effectiveness. In this architecture, the pre-ranking model is expected to be a lightweight approximation of the ranking model, which handles more candidates with strict latency requirements. Due to the gap in model capacity, the pre-ranking and ranking models usually generate inconsistent ranked results, thus hurting the overall system effectiveness. The paradigm of score alignment is proposed to regularize their raw scores to be consistent. However, it suffers from inevitable alignment errors and error amplification by bids when applied in online advertising. To this end, we introduce a consistency-oriented pre-ranking framework for online advertising, which employs a chunk-based sampling module and a plug-and-play rank alignment module to explicitly optimize consistency of ECPM-ranked results. A $\Delta NDCG$-based weighting mechanism is adopted to better distinguish the importance of inter-chunk samples in optimization. Both online and offline experiments have validated the superiority of our framework. When deployed in Taobao display advertising system, it achieves an improvement of up to +12.3\% CTR and +5.6\% RPM.

翻译：级联架构在大规模广告系统中被广泛采用，以平衡效率与效果。该架构中，预排序模型被视为排序模型的一种轻量级近似，需在严格延迟约束下处理更多候选物料。由于模型容量差异，预排序模型与排序模型通常产生不一致的排序结果，从而损害系统整体效果。现有范式通过分数对齐方法约束两者原始分数的一致性，但在在线广告场景中，该方法受制于固有对齐误差及竞价引发的误差放大问题。为此，我们提出一种面向在线广告的一致性导向预排序框架，该框架通过分块采样模块与即插即用的排序对齐模块，显式优化基于ECPM排序结果的一致性。采用基于ΔNDCG的加权机制更好地区分优化过程中块间样本的重要性。在线及离线实验均验证了本框架的优越性。在淘宝展示广告系统部署后，CTR实现+12.3%的提升，RPM实现+5.6%的提升。